Object detection using image classification models

ABSTRACT

In one aspect, the present disclosure relates to a method for or performing single-pass object detection and image classification. The method comprises receiving image data for an image in a system comprising a convolutional neural network (CNN), the CNN comprising a first convolutional layer, a last convolutional layer, and a fully connected layer; providing the image data to an input of the first convolutional layer; extracting multi-channel data from the output of the last convolutional layer; and summing the extracted data to generate a general activation map; and detecting a location of an object within the image by applying the general activation map to the image data.

BACKGROUND

Machine learning (ML) can be applied to various computer visionapplications, including object detection and image classification (or“image recognition”). General object detection can be used to locate anobject (e.g., a car or a bird) within an image, whereas imageclassification may involve a relatively fine-grained classification ofthe image (e.g., a 1969 Beetle, or an American Goldfinch). ConvolutionalNeural Networks (CNNs) are commonly used for both image classificationand object detection. A CNN is a class of deep, feed-forward artificialneural networks that has successfully been applied to analyzing visualimagery. Generalized object detection may require models that arerelatively large and computationally expensive, presenting a challengefor resource-constrained devices such as some smartphones and tabletcomputers. In contrast, image recognition may use relatively smallmodels and require relatively little processing.

SUMMARY

According to one aspect of the present disclosure, a method may performobject detection using image classification models. The method maycomprise: receiving image data for an image, wherein the image data isreceived in a system comprising a convolutional neural network (CNN),the CNN comprising an input layer, a first convolutional layer coupledto the input layer, a last convolutional layer, a fully connected layercoupled to the last convolution layer, and an output layer; providingthe image data to the input layer; extracting multi-channel data fromthe last convolutional layer; summing the multi-channel data to generatea general activation map; and detecting a location of an object withinthe image by applying the general activation map to the image data.

In some embodiments, generating the general activation map comprisesgenerating the general activation map without using class-specificweights. In some embodiments, detecting the location of an object withinthe image comprises identifying a bounding box within the image based oncomparing values within the general activation map to a predeterminedthreshold value. In some embodiments, detecting the location of anobject within the image comprises: interpolating data within the generalactivation map; and identifying a bounding box within the image usingthe interpolated data. In some embodiments, detecting the location of anobject within the image comprises upscaling the general activation mapbased on dimensions of the image.

According to another aspect of the present disclosure, a method may beused to augment an image using single-pass object detection and imageclassification. The method may comprise: receiving image data for animage, wherein the image data is received in a system comprising aconvolutional neural network (CNN), the CNN comprising an input layer, afirst convolutional layer coupled to the input layer, a lastconvolutional layer, a fully connected layer coupled to the lastconvolution layer, and an output layer; extracting multi-channel datafrom the output of the last convolutional layer; summing the extracteddata to generate a general activation map; detecting a location of anobject within the image by applying the general activation map to theimage data; receiving one or more classifications the output layer; anddisplaying the image and a content overlay, wherein a position of thecontent overlay relative to the image is determined using the detectedobject location, wherein the content overlay comprises informationdetermined by the one or more classifications.

In some embodiments, generating the general activation map comprisesgenerating the general activation map without using class-specificweights. In some embodiments, detecting the location of an object withinthe image comprises identifying a bounding box within the image based oncomparing values within the general activation map to a predeterminedthreshold value. In some embodiments, detecting the location of anobject within the image comprises: interpolating data within the generalactivation map; and identifying a bounding box within the image usingthe interpolated data. In some embodiments, detecting the location of anobject within the image comprises upscaling the general activation mapbased on dimensions of the image.

According to another aspect of the present disclosure, a system performssingle-pass object detection and image classification. The system maycomprise: a processor; a convolutional neural network (CNN) configuredfor execution on the processor, the CNN comprising a first convolutionallayer, a last convolutional layer, and a fully connected layer, whereinan output of the last convolutional layer is coupled to an input of thefully connected layer; an image ingestion module configured forexecution on the processor to receive image data for an image and toprovide the image data to an input of the first convolutional layer; anobject detection module configured to extract multi-channel data fromthe output of the last convolutional layer, sum the extracted data togenerate a general activation map, and to detect a location of an objectwithin the image by applying the general activation map to the imagedata; and an image augmentation module configured for execution on theprocessor to receive one or more classifications from an output of thefully connected layer and to display the image and a content overlay,wherein a position of the content overlay relative to the image isdetermined using the detected object location.

In some embodiments, generating the general activation map comprisesgenerating the general activation map without using class-specificweights. In some embodiments, detecting the location of an object withinthe image comprises identifying a bounding box within the image based oncomparing values within the general activation map to a predeterminedthreshold value. In some embodiments, the computer program code thatwhen executed on the processor causes the processor to execute a processoperable to: interpolate data within the general activation map; andidentify a bounding box within the image using the interpolated data. Insome embodiments, detecting the location of an object within the imagecomprises upscaling the general activation map based on dimensions ofthe image.

According to another aspect of the present disclosure, a non-transitorycomputer-readable medium may store program instructions that areexecutable to: receive image data for an image, wherein the image datais received in a system comprising a convolutional neural network (CNN),the CNN comprising a first convolutional layer, a last convolutionallayer, and a fully connected layer, wherein an output of the lastconvolutional layer is coupled to an input of the fully connected layer;provide the image data to an input of the first convolutional layer;extract multi-channel data from the output of the last convolutionallayer; sum the extracted data to generate a general activation map;detect a location of an object within the image by applying the generalactivation map to the image data; receive one or more classificationsfrom an output of the fully connected layer; and display the image and acontent overlay, wherein a position of the content overlay relative tothe image is determined using the detected object location.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objectives, features, and advantages of the disclosed subjectmatter can be more fully appreciated with reference to the followingdetailed description of the disclosed subject matter when considered inconnection with the following drawings.

FIG. 1 is a block diagram of a system for object detection and imageclassification, according to some embodiments of the present disclosure;

FIG. 2 is a diagram illustrating a convolutional neural network (CNN),according to some embodiments of the present disclosure;

FIGS. 3A, 3B, 4A, and 4B illustrate object detection techniques,according to some embodiments of the present disclosure;

FIG. 5 is a flow diagram showing processing that may occur within thesystem of FIG. 1, according to some embodiments of the presentdisclosure; and

FIG. 6 is a block diagram of a user device, according to an embodimentof the present disclosure.

The drawings are not necessarily to scale, or inclusive of all elementsof a system, emphasis instead generally being placed upon illustratingthe concepts, structures, and techniques sought to be protected herein.

DETAILED DESCRIPTION

Described herein are systems and methods for object detection usingimage classification models. In some embodiments, an image is processedthrough a single-pass convolutional neural network (CNN) trained forfine-grained image classification. Multi-channel data may be extractedfrom the last convolution layer of the CNN. The extracted data may besummed over all channels to produce a 2-dimensional matrix referredherein as a “general activation map.” the general activation maps mayindicate all the discriminative image regions used by the CNN toidentify classes. This map may be upscaled and used to see the“attention” of the model and used to perform general object detectionwithin the image. “Attention” of the model pertains to which segments ofthe image the model is paying most “attention” to based on valuescalculated up through the last convolutional layer that segments theimage into a grid (e.g., a 7×7 matrix). The model may give more“attention” to segments of the grid that have higher values, and thiscorresponds to the model predicting that an object is located withinthose segments. In some embodiments, object detection is performed in asingle-pass of the CNN, along with fine-grained image classification. Insome embodiments, a mobile app may use the image classification andobject detection information to provide augmented reality (AR)capability.

Some embodiments are described herein by way of example using images ofspecific objects, such as automobiles. The concepts and structuressought to be protected herein are not limited to any particular type ofimages.

Referring to FIG. 1, a system 100 may perform object detection and imageclassification, according to some embodiments of the present disclosure.The illustrative system 100 includes an image ingestion module 102, aconvolutional neural network (CNN) 104, a model database 106, an objectdetection module 108, and an image augmentation module 110. Each of themodules 102, 104, 108, 110 may include software and/or hardwareconfigured to perform the processing described herein. In someembodiments, the system modules 102, 104, 108, 110 may be embodied ascomputer program code executable on one or more processors (not shown).The modules 102, 104, 108, 110 may be coupled as shown in FIG. 1 or inany suitable manner. In some embodiments, the system 100 may beimplemented within a user device, such as user device 600 describedbelow in the context of FIG. 6.

The image ingestion module 102 receives an image 112 as input. The image112 may be provided in any suitable format, such as Joint PhotographicExperts Group (JPEG), Portable Network Graphics (PNG), or GraphicsInterchange Format (GIF). In some embodiments, the image ingestionmodule 102 includes an Application Programming Interface (API) via whichusers can upload images.

The image ingestion module 102 may receive images having an arbitrarywidth, height, and number of channels. For example, an image taken witha digital camera may have a width of 640 pixels, a height of 960 pixels,and three (3) channels (red, green, and blue) or one (1) channel(greyscale). The range of pixel values may vary depending on the imageformat or parameters of a specific image. For example, in some cases,each pixel may have a value between 0 to 255.

The image ingestion module 102 may convert the incoming image 112 into anormalized image data representation. In some embodiments, an image maybe represented as C 2-dimensional matrices stacked over each other (onefor each channel C), where each of the matrices is a W×H matrix of pixelvalues. The image ingestion module 102 may resize the image 112 to havedimensions W×H as needed. The values W and H may be determined by theCNN architecture. In one example, W=224 and H=224. The normalized imagedata may be stored in memory until it has been processed by the CNN 104.

The image data may be sent to an input layer of the CNN 104. Inresponse, the CNN 104 generates one or more classifications for theimage at an output layer. The CNN 104 may use a transfer-learned imageclassification model to perform “fine-grained” classifications. Forexample, the CNN may be trained to recognize a particular automobilemake, model, and/or year within the image. As another example, the modelmay be trained to recognize a particular species of bird within theimage. In some embodiments, the trained parameters of the CNN 104 may bestored within a non-volatile memory, such as within model database 106.In certain embodiments, the CNN 104 uses an architecture similar to onedescribed in A. Howard et al., “MobileNets: Efficient ConvolutionalNeural Networks for Mobile Vision Applications,” which is incorporatedherein by reference in its entirety.

As will be discussed further below in the context of FIG. 2, the CNN 104may include a plurality of convolutional layers arranged in series. Theobject detection module 108 may extract data from the last convolutionallayer in this series and use this data to perform object detectionwithin the image. In some embodiments, the object detection module 108may extract multi-channel data from the CNN 104 and sum over thechannels to generate a “general activation map.” This map may beupscaled and used to see the “attention” of the image classificationmodel, but without regard to individual classifications or weights. Forexample, if the CNN 104 is trained to classify particularmakes/models/years of automobiles within an image, the generalactivation map may approximately indicate where any automobile islocated with the image.

The object detection module 108 may generate, as output, informationdescribing the location of an object within the image 112. In someembodiments, the object detection module 108 outputs a bounding box thatlocates the object within the image 112.

The image augmentation module 110 may augment the original image togenerate an augmented image 112′ based on information received from theCNN 104 and the objection detection module 108. In some embodiments, theaugmented image 112′ includes the original image 112 overlaid with somecontent (“content overlay”) 116 that is based on CNN's fine-grainedimage classification. For example, returning to the car example, thecontent overlay 116 may include the text “1969 Beetle” if the CNN 104classifies an image of a car as having model “Beetle” and year “1969.”The object location information received from the object detectionmodule 108 may be used to position the content overlay 116 within the112′. For example, the content overlay 116 may be positioned along a topedge of a bounding box 118 determined by the object detection module108. The bounding box 118 is shown in FIG. 1 to aid in understanding,but could be omitted from the augmented image 112′.

In some embodiments, the system 100 may be implemented as a mobile appconfigured to run on a smartphone, tablet, or other mobile device suchas user device 600 of FIG. 6. In some embodiments, the input image 112be received from a mobile device camera, and the augmented output image112′ may be displayed on a mobile device display. In some embodiments,the app may include augmented reality (AR) capabilities. For example,the app may allow a user to point their mobile device camera at anobject and, in real-time or near real-time, see an augmented version ofthat object based on the object detection and image classification. Insome embodiments, the mobile app may augment the display withinformation pulled from a local or external data source. For example,the mobile app may use the CNN 104 to determine a vehicle'smake/model/year and then automatically retrieve and display loan rateinformation from a bank for that specific vehicle.

FIG. 2 shows an example of a convolutional neural network (CNN) 200,according to some embodiments of the present disclosure. The CNN 200 mayinclude an input layer (not shown), a plurality of convolutional layers202 a-202 d (202 generally), a global average pooling (GAP) layer 208, afully connected layer 210, and an output layer 212.

The convolutional layers 202 may be arranged in series as shown, with afirst convolutional layer 202 a coupled to the input layer, and a lastconvolutional layer 202 d coupled to the GAP layer 208. The layers ofthe CNN 200 may be implemented using any suitable hardware- orsoftware-based data structures and coupled using any suitable hardware-or software-based signal paths. The CNN 200 may be trained forfine-grained image classification. In particular, each of theconvolutional layers 202 along with the GPA 208 and fully connectedlayer 210 may have associated weights that are adjusted during trainingsuch that the output layer 212 accurately classifies images 112 receivedat the input layer.

Each convolutional layer 202 may include a fixed-size feature map thatcan be represented as a 3-dimensional matrix having dimensions W′×H′×D′,where D′ corresponds to the number of layers (or “depth”) within thatfeature map. The dimensions of the convolutional layers 202 may beirrespective of the images being classified. For example, the lastconvolution layer 202 may have width W′=7, height H′=7, and depthD′=1024, regardless of the size of the image 112.

After putting an image 112 through a single pass of a CNN 200,multi-channel data may be extracted from the last convolutional layer202 d. A general activation map 206 may be generated by summing 204 overall the channels of the extracted multi-channel data. For example, ifthe last convolution layer 202 d is structured as a 7×7 matrix with 1024channels, then the extracted multi-channel data would be a 7×7×1024matrix and the resulting general activation map 206 would be a 7×7matrix of values, where each value corresponds to a sum over 1024channels. In some embodiments, the general activation map 206 isnormalized such that each of its values is in the range [0, 1]. Thegeneral activation map 206 can be used to determine the location of anobject within the image. In some embodiments, the general activation map206 can be used to determine a bounding box for the object within theimage 112.

FIGS. 3A, 3B, 4A, and 4B illustrate object detection using a generalactivation map, such as general activation map 206 of FIG. 2. In each ofthese figures, a 7×7 general activation map is shown overlaid on animage and depicted using dashed lines. The overlaid map may be upscaledaccording to the dimensions of the image. For example, if the image hasdimensions 700×490 pixels, then the 7×7 general activation map may beupscaled such that each map element corresponds to 100×70 pixel area ofthe image. Each element of the general activation map has a valuecalculated by summing multi-channel data extracted from the CNN (e.g.,from convolutional layer 202 d in FIG. 2). The map values areillustrated in FIGS. 3A, 3B, 4A, and 4B by variations in color (i.e., asa heatmap), but which colors have been converted to greyscale for thisdisclosure.

Referring to FIG. 3A, an object may be detected within the image 300using a 7×7 general activation map. In some embodiments, each valuewithin the map is compared to a predetermined threshold value and abounding box 302 may be drawn around the elements of the map that havevalues above the threshold. The bounding box 302 approximatelycorresponds to the location of the object within the image 300. In someembodiments, the threshold value may be a parameter that can be adjustedbased on a desired granularity for the bounding box 302. For example,the threshold value may be lowered to increase the size of the boundingbox 302, or raised to decrease the size of the bounding box 302.

Referring to FIG. 3B, in some embodiments, the general activation mapmay be interpolated to achieve a more accurate (i.e., “tighter”)bounding box 302′ for the object. Any suitable interpolation techniquecan be used. In some embodiments, a predetermined threshold value isprovided as a parameter for the interpolation process. A bounding box302′ can then be drawn around the interpolated data, as shown. Incontrast to the bounding box 302 in FIG. 3A, the bounding box 302′ inFIG. 3B may not align with the upscaled general activation mapboundaries (i.e., the dashed lines in the figures).

FIGS. 4A and 4B illustrate object detection using another image 400. InFIG. 4A, a bounding box 402 may be determined by comparing values withinan upscaled 7×7 general activation map to a threshold value. In FIG. 4B,the general activation map may be interpolated and a different boundingbox 402′ may be established based on the interpolated data.

The techniques described herein provide approximate object detection tobe performed using a CNN that is designed and trained for imageclassification. In this sense, object detection can be achieved “forfree” (i.e., with minimal resources) making it well suited for mobileapps that may be resource constrained.

FIG. 5 is a flow diagram showing processing that may occur within thesystem of FIG. 1, according to some embodiments of the presentdisclosure. At block 502, image data may be received. In someembodiments, the image data may be converted from a specific imageformat (e.g., JPEG, PNG, or GIF) to a normalized (e.g., matrix-based)data representation.

At block 504, the image data may be provided to an input layer of aconvolutional neural network (CNN). The CNN may include the input layer,a plurality of convolutional layers, a fully connected layer, and anoutput layer, where a first convolutional layer is coupled to the inputlayer and a last convolutional layer is coupled to the fully connectedlayer.

At block 506, multi-channel data may be extracted from the lastconvolutional layer. At block 508, the extracted multi-channel data maybe summed over all channels to generate a 2-dimensional generalactivation map.

At block 510, the general activation map may be used to perform objectdetection within the image. In some embodiments, each value within thegeneral activation map is compared to a predetermined threshold value. Abounding box may be established around the values that are above thethreshold value. The bounding box may approximate the location of anobject within the image. In some embodiments, the general activation mapmay be interpolated to determine a more accurate bounding box. In someembodiments, the general activation map and/or the bounding box may beupscaled based on the dimensions of the image.

FIG. 6 shows a user device, according to an embodiment of the presentdisclosure. The illustrative user device 600 may include a memoryinterface 602, one or more data processors, image processors, centralprocessing units 604, and/or secure processing units 605, and aperipherals interface 606. The memory interface 602, the one or moreprocessors 604 and/or secure processors 605, and/or the peripheralsinterface 606 may be separate components or may be integrated in one ormore integrated circuits. The various components in the user device 600may be coupled by one or more communication buses or signal lines.

Sensors, devices, and subsystems may be coupled to the peripheralsinterface 606 to facilitate multiple functionalities. For example, amotion sensor 610, a light sensor 612, and a proximity sensor 614 may becoupled to the peripherals interface 606 to facilitate orientation,lighting, and proximity functions. Other sensors 616 may also beconnected to the peripherals interface 606, such as a global navigationsatellite system (GNSS) (e.g., GPS receiver), a temperature sensor, abiometric sensor, magnetometer, or other sensing device, to facilitaterelated functionalities.

A camera subsystem 620 and an optical sensor 622, e.g., a chargedcoupled device (CCD) or a complementary metal-oxide semiconductor (CMOS)optical sensor, may be utilized to facilitate camera functions, such asrecording photographs and video clips. The camera subsystem 620 and theoptical sensor 622 may be used to collect images of a user to be usedduring authentication of a user, e.g., by performing facial recognitionanalysis.

Communication functions may be facilitated through one or more wiredand/or wireless communication subsystems 624, which can include radiofrequency receivers and transmitters and/or optical (e.g., infrared)receivers and transmitters. For example, the Bluetooth (e.g., Bluteoothlow energy (BTLE)) and/or WiFi communications described herein may behandled by wireless communication subsystems 624. The specific designand implementation of the communication subsystems 624 may depend on thecommunication network(s) over which the user device 600 is intended tooperate. For example, the user device 600 may include communicationsubsystems 624 designed to operate over a GSM network, a GPRS network,an EDGE network, a WiFi or WiMax network, and a Bluetooth™ network. Forexample, the wireless communication subsystems 624 may include hostingprotocols such that the device 6 can be configured as a base station forother wireless devices and/or to provide a WiFi service.

An audio subsystem 626 may be coupled to a speaker 628 and a microphone630 to facilitate voice-enabled functions, such as speaker recognition,voice replication, digital recording, and telephony functions. The audiosubsystem 626 may be configured to facilitate processing voice commands,voiceprinting, and voice authentication, for example.

The I/O subsystem 640 may include a touch-surface controller 642 and/orother input controller(s) 644. The touch-surface controller 642 may becoupled to a touch surface 646. The touch surface 646 and touch-surfacecontroller 642 may, for example, detect contact and movement or breakthereof using any of a plurality of touch sensitivity technologies,including but not limited to capacitive, resistive, infrared, andsurface acoustic wave technologies, as well as other proximity sensorarrays or other elements for determining one or more points of contactwith the touch surface 646.

The other input controller(s) 644 may be coupled to other input/controldevices 648, such as one or more buttons, rocker switches, thumb-wheel,infrared port, USB port, and/or a pointer device such as a stylus. Theone or more buttons (not shown) may include an up/down button for volumecontrol of the speaker 628 and/or the microphone 630.

In some implementations, a pressing of the button for a first durationmay disengage a lock of the touch surface 646; and a pressing of thebutton for a second duration that is longer than the first duration mayturn power to the user device 600 on or off. Pressing the button for athird duration may activate a voice control, or voice command, modulethat enables the user to speak commands into the microphone 630 to causethe device to execute the spoken command. The user may customize afunctionality of one or more of the buttons. The touch surface 646 can,for example, also be used to implement virtual or soft buttons and/or akeyboard.

In some implementations, the user device 600 may present recorded audioand/or video files, such as MP3, AAC, and MPEG files. In someimplementations, the user device 600 may include the functionality of anMP3 player, such as an iPod™. The user device 600 may, therefore,include a 36-pin connector and/or 8-pin connector that is compatiblewith the iPod. Other input/output and control devices may also be used.

The memory interface 602 may be coupled to memory 650. The memory 650may include high-speed random access memory and/or non-volatile memory,such as one or more magnetic disk storage devices, one or more opticalstorage devices, and/or flash memory (e.g., NAND, NOR). The memory 650may store an operating system 652, such as Darwin, RTXC, LINUX, UNIX, OSX, WINDOWS, or an embedded operating system such as VxWorks.

The operating system 652 may include instructions for handling basicsystem services and for performing hardware dependent tasks. In someimplementations, the operating system 652 may be a kernel (e.g., UNIXkernel). In some implementations, the operating system 652 may includeinstructions for performing voice authentication.

The memory 650 may also store communication instructions 654 tofacilitate communicating with one or more additional devices, one ormore computers and/or one or more servers. The memory 650 may includegraphical user interface instructions 656 to facilitate graphic userinterface processing; sensor processing instructions 658 to facilitatesensor-related processing and functions; phone instructions 660 tofacilitate phone-related processes and functions; electronic messaginginstructions 662 to facilitate electronic-messaging related processesand functions; web browsing instructions 664 to facilitate webbrowsing-related processes and functions; media processing instructions666 to facilitate media processing-related processes and functions;GNSS/Navigation instructions 668 to facilitate GNSS andnavigation-related processes and instructions; and/or camerainstructions 670 to facilitate camera-related processes and functions.

The memory 650 may store instructions and data 672 for an augmentedreality (AR) app, such as discussed above in conjunction with FIG. 1.For example, the memory 650 may store instructions corresponding to oneor more of the modules 102, 104, 108, 110 shown in FIG. 1, along withthe data for one or more machine learning models 106 and/or data forimages 112 being processed thereby.

Each of the above identified instructions and applications maycorrespond to a set of instructions for performing one or more functionsdescribed herein. These instructions need not be implemented as separatesoftware programs, procedures, or modules. The memory 650 may includeadditional instructions or fewer instructions. Furthermore, variousfunctions of the user device may be implemented in hardware and/or insoftware, including in one or more signal processing and/or applicationspecific integrated circuits.

In some embodiments, processor 604 may perform processing includingexecuting instructions stored in memory 650, and secure processor 605may perform some processing in a secure environment that may beinaccessible to other components of user device 600. For example, secureprocessor 605 may include cryptographic algorithms on board, hardwareencryption, and physical tamper proofing. Secure processor 605 may bemanufactured in secure facilities. Secure processor 605 may encryptdata/challenges from external devices. Secure processor 605 may encryptentire data packages that may be sent from user device 600 to thenetwork. Secure processor 605 may separate a valid user/external devicefrom a spoofed one, since a hacked or spoofed device may not have theprivate keys necessary to encrypt/decrypt, hash, or digitally sign data,as described herein.

It is to be understood that the disclosed subject matter is not limitedin its application to the details of construction and to thearrangements of the components set forth in the following description orillustrated in the drawings. The disclosed subject matter is capable ofother embodiments and of being practiced and carried out in variousways. Also, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting. As such, those skilled in the art will appreciatethat the conception, upon which this disclosure is based, may readily beutilized as a basis for the designing of other structures, methods, andsystems for carrying out the several purposes of the disclosed subjectmatter. It is important, therefore, that the claims be regarded asincluding such equivalent constructions insofar as they do not departfrom the spirit and scope of the disclosed subject matter.

Although the disclosed subject matter has been described and illustratedin the foregoing exemplary embodiments, it is understood that thepresent disclosure has been made only by way of example, and thatnumerous changes in the details of implementation of the disclosedsubject matter may be made without departing from the spirit and scopeof the disclosed subject matter.

1-20. (canceled)
 21. A method for performing object detectioncomprising: providing an image to a single-pass convolutional neuralnetwork (CNN); extracting multi-channel data from a last convolutionallayer of the CNN; generating a two-dimensional general activation mapusing the multi-channel data; and detecting a location of an objectwithin the image by applying the general activation map to the image.22. The method of claim 21 wherein generating the general activation mapcomprises generating the general activation map without usingclass-specific weights.
 23. The method of claim 21 wherein detecting thelocation of an object within the image comprises identifying a boundingbox within the image based on comparing values within the generalactivation map to a predetermined threshold value.
 24. The method ofclaim 21 wherein detecting the location of an object within the imagecomprises: interpolating data within the general activation map; andidentifying a bounding box within the image using the interpolated data.25. The method of claim 21 wherein detecting the location of an objectwithin the image comprises upscaling the general activation map based ondimensions of the image.
 26. A method for augmenting an image usingsingle-pass object detection and image classification, the methodcomprising: providing an image to a single-pass convolutional neuralnetwork (CNN); receiving, from the CNN, convolutional data and one ormore classifications; generating a two-dimensional general activationmap using the convolutional data; detecting a location of an objectwithin the image by applying the general activation map to the image;displaying the image and a content overlay, wherein a position of thecontent overlay relative to the image is determined using the detectedobject location, wherein the content overlay comprises informationdetermined by the one or more classifications.
 27. The method of claim26 wherein generating the general activation map comprises generatingthe general activation map without using class-specific weights.
 28. Themethod of claim 26 wherein detecting the location of an object withinthe image comprises identifying a bounding box within the image based oncomparing values within the general activation map to a predeterminedthreshold value.
 29. The method of claim 26 wherein detecting thelocation of an object within the image comprises: interpolating datawithin the general activation map; and identifying a bounding box withinthe image using the interpolated data.
 30. The method of claim 26wherein detecting the location of an object within the image comprisesupscaling the general activation map based on dimensions of the image.31. A system for performing single-pass object detection and imageclassification, the system comprising: a processor; a non-volatilememory storing computer instructions that when executed on the processorcause the processor to: provide an image to a single-pass convolutionalneural network (CNN); extract multi-channel data from a lastconvolutional layer of the CNN; generate a two-dimensional generalactivation map using the multi-channel data; and detect a location of anobject within the image by applying the general activation map to theimage.
 32. The system of claim 31 wherein computer instructions thatwhen executed on the processor cause the processor to: generate thegeneral activation map without using class-specific weights.
 33. Thesystem of claim 31 wherein computer instructions that when executed onthe processor cause the processor to: detect the location of an objectwithin the image by identifying a bounding box within the image based oncomparing values within the general activation map to a predeterminedthreshold value.
 34. The system of claim 31 wherein the computerinstructions that when executed on the processor cause the processor to:interpolate data within the general activation map; and identify abounding box within the image using the interpolated data.
 35. Thesystem of claim 31 wherein the computer instructions that when executedon the processor cause the processor to: detect the location of anobject within the image by upscaling the general activation map based ondimensions of the image.